System and method for identifying associated subjects from location histories

ABSTRACT

Systems and methods to track the respective locations of subjects over time. The system identifies subjects who, overtime, were co-located with one another suggesting they are associated with one another, and the pairs are analyzed. For each of the subjects, the system produces a vector that quantifies the subject&#39;s location history by including a respective weight for each combination of a time interval with a geographical area. The vectors are compared using a distance metric, and any pair of subjects whose vectors are sufficiently close are flagged as being an associated pair. The respective vector belonging to each subject is normalized to account for the total number of other subjects who were co-located with the subject. For each interval-area pair, the system may compute the frequency of the interval-area pair, and then divide each weight that corresponds to the interval-area pair by the frequency of the interval-area pair.

This application is a continuation of U.S. patent application Ser. No.17/020,947, filed Sep. 15, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/358,827, filed Mar. 20, 2019, now U.S. Pat. No.10,812,934 entitled “SYSTEM AND METHOD FOR IDENTIFYING ASSOCIATEDSUBJECTS FROM LOCATION HISTORIES,” which is incorporated herein byreference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to the monitoring of subjectsof interest, such as for law-enforcement or security purposes.

BACKGROUND OF THE DISCLOSURE

In many cases, law-enforcement or security agencies may track thelocation of a subject of interest over time.

Ramos, Juan, “Using tf-idf to determine word relevance in documentqueries,” Proceedings of the first instructional conference on machinelearning, Vol. 242, 2003, examines the results of applying TermFrequency Inverse Document Frequency (TF-IDF) to determine what words ina corpus of documents might be more favorable to use in a query.

Quercia, Daniele, et al., “Recommending social events from mobile phonelocation data,” Data Mining (ICDM), 2010 IEEE 10th InternationalConference on. IEEE, 2010, describes sampling location estimations ofone million mobile phone users in Greater Boston, combining the samplewith social events in the same area, inferring the social eventsattended by 2,519 residents, and, upon this data, testing a variety ofalgorithms for recommending social events.

Bao, Jie et al., “Location-based and preference-aware recommendationusing sparse geo-social networking data,” Proceedings of the 20thinternational conference on advances in geographic information systems,ACM, 2012, presents a location-based and preference-aware recommendersystem that offers a particular user a set of venues (such asrestaurants) within a geospatial range with the consideration ofboth: 1) user preferences, which are automatically learned from herlocation history and 2) social opinions, which are mined from thelocation histories of the local experts.

Rekimoto, Jun, et al., “LifeTag: WiFi-based continuous location loggingfor life pattern analysis,” LoCA., Vol. 2007, proposes a WiFi-basedlocation detection technology for location logging.

SUMMARY OF THE DISCLOSURE

There is provided, in accordance with some embodiments of the presentinvention, a system that includes a communication interface and aprocessor. The processor is configured to receive, via the communicationinterface, tracking data that indicate respective locations of aplurality of subjects within a plurality of geographic areas and duringa plurality of time intervals. The processor is further configured to,responsively to the tracking data, calculate a plurality of weights,including a respective weight corresponding to each combination of asubject, a geographic area, and a time interval, which quantifies adegree to which the tracking data indicate that the subject was locatedin the geographic area during the time interval. The processor isfurther configured to select a plurality of interval-area pairs, each ofwhich includes a respective one of the geographic areas and a respectiveone of the time intervals. The processor is further configured to, foreach of the selected interval-area pairs, normalize a respective subsetof the weights that correspond to respective combinations of thesubjects with the selected interval-area pair, and to construct aplurality of vectors, including, for each of the subjects, a respectivevector that includes those of the normalized weights that correspond torespective combinations of the subject with the selected interval-areapairs. The processor is further configured to calculate respectivemeasures of similarity between one or more pairs of the vectors, and togenerate, in response to the measures of similarity, an outputindicating respective pairings of one or more pairs of the subjects.

In some embodiments, the processor is configured to calculate each ofthe weights responsively to a percentage of the time interval duringwhich the subject was located in the geographic area that is indicatedby the tracking data.

In some embodiments, the processor is configured to calculate each ofthe weights by:

calculating a level of confidence that the tracking data indicate thatthe subject was located in the geographic area during the time interval,and

calculating the weight responsively to the level of confidence.

In some embodiments, the processor is configured to select theinterval-area pairs by:

for each of the interval-area pairs:

-   -   computing a sum of those of the weights that correspond to        respective combinations of the subjects and the interval-area        pair, and    -   selecting the interval-area pair responsively to the sum.

In some embodiments, the processor is configured to select theinterval-area pairs by:

for each of the interval-area pairs:

-   -   computing a number of those of the weights that correspond to        respective combinations of the subjects and the interval-area        pair and are greater than zero, and    -   selecting the interval-area pair responsively to the number.

In some embodiments, the processor is configured to normalize therespective subset of the weights by:

calculating a normalizing factor as (i) an increasing function of a sumof the subset of the weights, and (ii) a decreasing function of a totalsum of the weights, and

normalizing the subset of the weights by the normalizing factor.

In some embodiments, the processor is configured to normalize therespective subset of the weights by:

calculating a normalizing factor as (i) an increasing function of anumber of those of the weights in the subset that are greater than zero,and (ii) a decreasing function of a total number of the subjects, and

normalizing each of the weights in the subset by the normalizing factor.

In some embodiments, the processor is further configured to,subsequently to constructing the vectors, reduce a dimensionality of thevectors.

In some embodiments, the processor is further configured to, prior tocalculating the measures of similarity:

cluster the vectors into a plurality of different clusters, and

subsequently to clustering the vectors, select the pairs of the vectorsin response to each of the pairs of the vectors being contained within asame one of the clusters or within respective ones of the clusters thatare within a predefined threshold distance of one another.

There is further provided, in accordance with some embodiments of thepresent invention, a method that includes receiving tracking data thatindicate respective locations of a plurality of subjects within aplurality of geographic areas and during a plurality of time intervals.The method further includes, responsively to the tracking data,calculating a plurality of weights, including a respective weightcorresponding to each combination of a subject, a geographic area, and atime interval, which quantifies a degree to which the tracking dataindicate that the subject was located in the geographic area during thetime interval. The method further includes selecting a plurality ofinterval-area pairs, each of which includes a respective one of thegeographic areas and a respective one of the time intervals. The methodfurther includes, for each of the selected interval-area pairs,normalizing a respective subset of the weights that correspond torespective combinations of the subjects and the interval-area pair, andconstructing a plurality of vectors, including, for each of thesubjects, a respective vector that includes those of the normalizedweights that correspond to respective combinations of the subject andthe selected interval-area pairs. The method further includescalculating respective measures of similarity between one or more pairsof the vectors, and, in response to the measures of similarity,generating an output indicating respective pairings of one or more pairsof the subjects.

There is further provided, in accordance with some embodiments of thepresent invention, a computer software product including a tangiblenon-transitory computer-readable medium in which program instructionsare stored. The instructions, when read by a processor, cause theprocessor to receive tracking data that indicate respective locations ofa plurality of subjects within a plurality of geographic areas andduring a plurality of time intervals, and, responsively to the trackingdata, calculate a plurality of weights, including a respective weightcorresponding to each combination of a subject, a geographic area, and atime interval, which quantifies a degree to which the tracking dataindicate that the subject was located in the geographic area during thetime interval. The instructions further cause the processor to select aplurality of interval-area pairs, each of which includes a respectiveone of the geographic areas and a respective one of the time intervals.The instructions further cause the processor to, for each of theselected interval-area pairs, normalize a respective subset of theweights that correspond to respective combinations of the subjects andthe interval-area pair, and construct a plurality of vectors, including,for each of the subjects, a respective vector that includes those of thenormalized weights that correspond to respective combinations of thesubject and the selected interval-area pairs. The instructions furthercause the processor to calculate respective measures of similaritybetween one or more pairs of the vectors, and, in response to themeasures of similarity, generate an output indicating respectivepairings of one or more pairs of the subjects.

The present disclosure will be more fully understood from the followingdetailed description of embodiments thereof, taken together with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system for identifyingassociations between subjects, in accordance with some embodiments ofthe present disclosure;

FIG. 2 is a schematic illustration of a plurality of location-historyvectors computed from location information, in accordance with someembodiments of the present disclosure; and

FIG. 3, which is a flow diagram for a method for identifying associatedpairs of subjects, in accordance with some embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

In many cases, law-enforcement or security agencies may wish to identifypeople who are associated, in some capacity, with a subject of interest(SOI), such as family members or coworkers of the SOI.

To facilitate identifying such associations, embodiments of the presentdisclosure provide a system configured to track the respective locationsof multiple subjects over time. Based on the tracking, the systemidentifies each pair of subjects who, over the tracking period, wereco-located with one another in a manner that suggests that the pair ofsubjects are associated with one another, and then flags each of thesepairs for subsequent analysis.

Typically, for each of the tracked subjects, the system produces avector that quantifies the subject's location history by including arespective weight for each relevant combination of a time interval witha geographical area. For example, if a particular subject was present inarea XYZ for 80% of time interval ABC, the subject's location-historyvector may include a weight of 0.8 associated with the interval-areapair ABC-XYZ. Subsequently to constructing the subjects' vectors, thevectors are compared to each other using a suitable distance metric (or“similarity measure”), and any pair of subjects whose vectors aresufficiently close to one another are flagged as being a potentiallyassociated pair.

Typically, before comparing the vectors, the respective vector belongingto each subject is normalized to account for the total number of othersubjects who were co-located with the subject, such as to reduce thenumber of spurious associations that are identified. Thus, for example,if two particular subjects were located together in a stadium, duringgame time, with thousands of other subjects, the system is less likelyto identify an association between the two subjects, relative to if thetwo subjects were co-located in the stadium while the stadium was almostempty.

For example, for each interval-area pair, the system may compute thefrequency of the interval-area pair (typically by computing thepercentage of the vectors in which the interval-area pair appears with anon-zero weight), and then divide each weight that corresponds to theinterval-area pair by the frequency of the interval-area pair. Thus, forexample, assuming that the interval-area pair ABC-XYZ has a frequency of0.00001 (indicating that only 0.001% of the tracked subjects were inarea XYZ during time interval ABC), a weight of 0.8 for ABC-XYZ may beconverted to a relatively large vector element of 0.8/0.00001=80000,such that the contribution of ABC-XYZ to the similarity measure may berelatively large. In contrast, if the frequency of ABC-XYZ were 0.01(indicating that a relatively large percentage—1%—of the trackedsubjects were in area XYZ during time interval ABC), the weight of 0.8would be converted to a relatively small vector element of 80, such thatABC-XYZ would contribute less to the similarity measure.

System Description

Reference is initially made to FIG. 1, which is a schematic illustrationof a system 20 for identifying associations between subjects 40, inaccordance with some embodiments of the present disclosure. System 20comprises at least one communication interface 22, such as a networkinterface controller (NIC) 22 a, and a processor 24. As described indetail below, processor 24 is configured to track the respectivelocations of subjects 40 within a plurality of geographic areas andduring a plurality of time intervals, using tracking data received viacommunication interface 22. Based on the tracking, the processoridentifies pairs of subjects 40 who are associated with (or “relatedto”) one another.

In some embodiments, the processor identifies the subjects' locationsfrom tracking data received from one or more taps 36 located in acellular network. For example, in a Universal

Mobile Telecommunications System (UMTS) cellular network, a tap 36 maybe located between the base stations 34 and the radio network controller(RNC) 38 of the radio access network (RAN) 32 of the network. In suchembodiments, tap 36 may continually receive from base stations 34, foreach subject 40, parameters related to the subject's cellular device(e.g., ratios of signal to interference, received signal code powers,and round trip times), in association with an identifier of the device.The tap may then communicate these parameters to the processor. Theseparameters indicate the location of the subject, in that, based on theseparameters, the processor may compute (e.g., using triangulation) thelocation of the device, and hence, the location of the subject.Alternatively or additionally, to track the subjects' locations,processor 24 may receive information from a tap at any other location inthe cellular network, such as within the core network of the cellularnetwork.

Alternatively or additionally to receiving tracking data from a cellularnetwork, processor 24 may receive tracking data from tracking sensors 42located, for example, within the vehicle of a subject or on the clothingof a subject. Each sensor 42 continually communicates the sensor'slocation to the processor, such that, as the sensor moves with thesubject, the processor may track the subject's location.

Alternatively or additionally, other sensors 44, such as an imagingsensor or other type of electronic sensor located at the entrance to aparticular area, may report the movement of a particular subject into,or from, the area. Thus, for example, responsively to a subject swipinga card to gain entry to his place of work, a sensor 44 may report thesubject's entry to the processor. Alternatively or additionally, theprocessor may monitor communication traffic, e.g., over the Internet,and, from this traffic, extract information that relates to thesubjects' locations.

Alternatively or additionally, any other suitable tracking technique maybe used. In general, tracking data may be received by processor 24wirelessly and/or wiredly, using any suitable communication protocol,such as the Internet Protocol (IP). Communication interface 22 maycomprise any suitable hardware or software elements, alternatively oradditionally to NIC 22 a, for facilitating receipt of these data.

Prior to, while, or subsequently to tracking the subjects, the processordiscretizes the time over which the subjects are tracked. In particular,the processor divides the time over which the subjects are tracked intointervals, each of which may be of any suitable length, such as 30minutes, 1 hour, or two hours. In general, the time intervals are notnecessarily of equal length, i.e., some of the time intervals may belonger than others. (For example, nighttime intervals may be longer thandaytime intervals.)

Similarly, the processor discretizes the entire area over which thesubjects are tracked, by partitioning this entire area into separategeographic areas, e.g., using the Military Grid Reference System (MGRS),and/or any other suitable partitioning technique. In general, thegeographic areas are not necessarily of equal size, i.e., some of theareas may be larger than others. For example, one geographic area mayspan a large outdoor park, another may cover a smaller city block, andyet another may include only a single building.

Typically, in addition to communication interface 22 and processor 24,system 20 comprises a display 26. Processor 24 may be configured todisplay, on display 26, any relevant output, such as an output thatindicates that a given pair of the subjects are associated with oneanother, and/or a likelihood of such an association. System 20 mayfurther comprise one or more input devices, such as a keyboard 28 and amouse 30, which may be used by a user to interact with the system.

In some embodiments, the functionality of processor 24, as describedherein, is implemented solely in hardware, e.g., using one or moreApplication-Specific Integrated Circuits (ASICs) or Field-ProgrammableGate Arrays (FPGAs). In other embodiments, the functionality ofprocessor 24 is implemented at least partly in software. For example, insome embodiments, processor 24 may be embodied as a programmed digitalcomputing device comprising at least a central processing unit (CPU) andrandom access memory (RAM). Program code, including software programs,and/or data are loaded into the RAM for execution and processing by theCPU. The program code and/or data may be downloaded to the processor inelectronic form, over a network, for example. Alternatively oradditionally, the program code and/or data may be provided and/or storedon non-transitory tangible media, such as magnetic, optical, orelectronic memory. Such program code and/or data, when provided to theprocessor, produce a machine or special-purpose computer, configured toperform the tasks described herein.

Calculating Vectors of Normalized Weights

Reference is now made to FIG. 2, which is a schematic illustration of aplurality of location-history vectors 50 computed from locationinformation 46 by processor 24, in accordance with some embodiments ofthe present disclosure.

Further to receiving the tracking data as described above with referenceto FIG. 1, processor 24 extracts, from the data, location information46. In particular, in response to the tracking data, the processorcalculates, for each of the subjects, for each of the time intervals,for each of the geographic areas, a weight 48 that quantifies the degreeto which the tracking data indicate that the subject was located in thegeographic area during the time interval. (Hence, each weight 48 may besaid to correspond to the combination of one of the subjects, one of thegeographic areas, and one of the time intervals.) Thus, for example,given “S” subjects, “G” geographic areas, and “T” time intervals, theprocessor calculates a total of S*G*T weights 48. Location information46 includes all of the computed weights.

By way of example, FIG. 2 shows a hypothetical snippet of locationinformation 46, in which various weights are shown for (i) threesubjects: Subject A, Subject B, and Subject C; (ii) three intervals:Interval 1, Interval 2, Interval 3; and (iii) three areas: Area 1, Area2, Area 3. (Thus, a total of 27 weights are shown.) Although FIG. 2assumes that weights 48 are on a scale of 0 to 1, it is noted that theweights may, alternatively, span any other suitable range.

Typically, each of the weights is calculated in response to thepercentage of the time interval during which the subject was located inthe geographic area that is indicated by the tracking data.Alternatively or additionally, the processor may calculate a level ofconfidence that the tracking data indicate that the subject was locatedin the geographic area during the time interval, and then calculate theweight responsively to the level of confidence. For example, aparticular tracking signal received by the processor may indicate thelocation of the subject with relatively little precision, such that thesignal effectively specifies a range of possible locations for thesubject, spanning a plurality of geographic areas. In such a case, theprocessor may calculate a respective level of confidence for each of thegeographic areas, responsively to the percentage of the range that isincluded within the geographic area. The processor may then calculatethe respective weights for the geographic areas, based on these levelsof confidence.

Typically, each weight is calculated in response to both of theaforementioned factors, e.g., by multiplying the level of confidencewith the percentage of the time interval. For example, in response toascertaining, based on the tracking data, that Subject A was located inArea 1 during 100% of Interval 3, with a level of confidence of 100%,the processor may assign a maximum weight of 1 to Subject A for theinterval-area pair that includes Interval 3 and Area 1, indicated by thenotation “(Interval 3, Area 1).” Conversely, if the processor does notreceive any indication that Subject A was present in Area 1 duringInterval 2, the processor may assign a minimum weight of 0 to Subject Afor the interval-area pair (Interval 2, Area 1). As another example, theprocessor may assign an intermediate weight of 0.5 to Subject C for theinterval-area pair (Interval 1, Area 1), if (i) the processorascertains, with a level of confidence of 100%, that Subject C waslocated in Area 1 for 50% of Interval 1, or (ii) the processorascertains that Subject C was located in Area 1 for 100% of Interval 1,but with a level of confidence of only 50%.

Subsequently to collecting location information 46, the processor (i)selects at least some of the interval-area pairs, (ii) normalizes thoseweights 48 corresponding to the selected interval-area pairs, such as toyield a plurality of normalized weights 52, and then (iii) constructs arespective one-dimensional or two-dimensional location-history vector 50for each of the tracked subjects, by assigning, to the vector, thosenormalized weights 52 that correspond to the subject (i.e., thatcorrespond to respective combinations of the subject with the selectedinterval-area pairs). In other words, the processor constructs, for eachof the tracked subjects, a vector 50 of normalized weights 52, whereeach normalized weight 52 corresponds to a different respective selectedinterval-area pair. For example, FIG. 2 shows, for Subject A, anormalized weight of 12,345 for the interval-area pair (Interval 1, Area1), indicated in FIG. 2 by the notation “(I1, A1).” The paragraphs belowprovide further description with regards to the construction of vectors50.

Typically, vectors 50, which are of the same size for each of thesubjects, do not contain a normalized weight for each of theinterval-area pairs. Rather, prior to constructing vectors 50, theprocessor selects a subset of the interval-area pairs from the entireset of interval-area pairs, responsively to weights 48. In particular,based on weights 48, the processor selects those interval-area pairsthat provide more information than the other interval-area pairs, asdescribed below. The processor then normalizes the weights thatcorrespond to the selected interval-area pairs, and assigns thesenormalized weights to vectors 50, while ignoring the other weights.Thus, for example, given 1000 different geographic areas and 1000different time intervals, the number of elements in each of vectors 50may be much less than 1,000,000; for example, this number may be between1,000 and 10,000.

In some embodiments, to select the subset of the interval-area pairs,the processor first computes, for each interval-area pair, the sum ofthe subset of the weights that correspond to the interval-area pair(i.e., that correspond to respective combinations of the subjects withthe interval-area pair). For example (ignoring, for ease of description,any subjects other than the three subjects shown in FIG. 2), theprocessor may compute a sum of 1.3 (=0.8+0.5) for (I1,A1). Next, inresponse to the sums, the processor selects the subset of theinterval-area pairs. For example, the processor may select thoseinterval-area pairs whose respective weight sums are below a predefinedthreshold number. (This threshold number may be fixed; alternatively, itmay be variable, in that it may be calculated based on a percentile ofthe weight sums.) The processor may thus reject those interval-areapairs that provide relatively little information by virtue of beingincluded in the respective location histories of a relatively largenumber of subjects. Alternatively or additionally, the processor mayrequire that the respective weight sums of the selected interval-areapairs be above another (fixed or variable) predefined threshold, thusrejecting those interval-area pairs that provide relatively littleinformation by virtue of being included in the respective locationhistories of only a small number of subjects.

Alternatively, to select the subset of the interval-area pairs, theprocessor may compute, for each interval-area pair, the number ofweights that correspond to the interval-area pair and are greater thanzero. For example (ignoring, for ease of description, any subjects otherthan the three shown in FIG. 2), the processor may count two non-zeroweights for (I1,A1). Next, the processor may select the subsetresponsively to the numbers, by selecting those interval-area pairswhose number of non-zero weights is less than a first threshold, and/orgreater than a second threshold. (This technique is effectively avariation of the previous technique, in which all of the non-zeroweights are rounded to one prior to computing the weight sums.)

Subsequently, for each of the selected interval-area pairs, theprocessor calculates a respective normalizing factor, which quantifiesthe commonality, or “frequency,” of the interval-area pair in thesubjects' location histories. The processor then normalizes, by thenormalizing factor, the subset of the weights corresponding to theinterval-area pair. For example, the processor may compute eachnormalized weight 52 by dividing the corresponding “raw” weight 48 bythe normalizing factor. In general, the normalized weights provide moreinformation as to any potential relationships between the subjects,relative to the raw weights.

For example, assuming the weights are normalized by being divided by thenormalizing factors, FIG. 2 implies a normalizing factor of 0.0017 for(I3, A1), as evidenced by the normalized weight of 600 for both SubjectA and Subject B (1/0.0017=600). On the other hand, FIG. 2 implies anormalizing factor of only 0.0000648 for (I1, A1), as evidenced by thenormalized weight of 12,345 for Subject A (0.8/0.0000648=12,345). Thedifference between these normalizing factors indicates that (I1, A1) ismuch less common than (I3, A1), such that the presence of two differentsubjects in Area 1 during Interval 1 is more indicative of a potentialrelationship between the two subjects, relative to the presence of twodifferent subjects in Area 1 during Interval 3. Hence, by normalizingthe weights, the processor causes the element in vector 50 correspondingto (I1, A1) to have greater significance than the element correspondingto (I3, A1).

In some embodiments, each normalizing factor is calculated as (i) anincreasing function of the sum of those of the weights that correspondto the interval-area pair, and (ii) a decreasing function of the totalsum of the weights. For example, using the notation w_(ijk) to indicatethe weight for the i^(th) subject, the j^(th) interval, and the k^(th)area, the normalizing factor may calculated, for any specificinterval-area pair (I_(M), A_(N)), as (Σ_(i)w_(iMN)/Σ_(i,j,k)w_(ijk)),or as the logarithm of this ratio.

For example (ignoring, for ease of description, any subjects andinterval-area pairs other than those shown in FIG. 2), the processor maycalculate a sum of 1.3 for (I1, A1), a sum of 0.5 for (I2, A1), a sum of2.9 for (I3, A1), a sum of 1.2 for (I1, A2), a sum of 0.7 for (I2, A2),a sum of 0.5 for (I2, A3), and a sum of zero for each of the otherinterval-area pairs. The total sum of the weights is thus the sum ofthese sums, i.e., 7.1. Accordingly, the processor may, for example,calculate a normalizing factor of 0.183 (=1.3/7.1) for (I1, A1). (Toavoid any confusion, it is noted that this normalizing factor differsfrom the implied normalizing factor described above, since thisnormalizing factor does not account for any subjects or interval-areapairs not shown in FIG. 2, and, furthermore, assumes one particularcalculation technique.)

Alternatively, each normalizing factor may be calculated as (i) anincreasing function of the number of those of the weights thatcorrespond to the interval-area pair and are greater than zero (which isequivalent to the number of subjects for whom the weight thatcorresponds to the interval-area pair is greater than zero), and (ii) adecreasing function of the total number of subjects. For example, forany specific interval-area pair (I_(M), A_(N)), the normalizing factormay be calculated as (Σ_(i)(w_(iMN)>0)/S), where S is the total numberof subjects, or the logarithm of this ratio. For example (ignoring, forease of description, any subjects other than those shown in FIG. 2), theprocessor may calculate a normalizing factor of 0.67 (=2/3) for (I1,A1).

Subsequently to assigning the normalized weights to vectors 50, theprocessor may reduce the dimensionality of the vectors, e.g., usingPrincipal Component Analysis (PCA). Alternatively or additionally, theprocessor may further normalize each vector, e.g., such that the squaresof the weights in the vector sum to one.

Identifying Associated Subjects

Subsequently to computing vectors 50, the processor calculates thecosine similarity, Euclidean distance, or any other suitable measure ofsimilarity between each of one or more pairs of the vectors. Theprocessor then generates an output (e.g., a visual output on display 26)indicating respective pairings of one or more pairs of the subjects, inresponse to the calculated measures of similarity.

For example, in response to a query from a user regarding a particularSOI, the processor may calculate respective measures of similaritybetween (i) the vector belonging to the SOI, and (ii) the respectivevectors belonging to one or more other subjects. In response toascertaining that the measure of similarity between the vector of theSOI and any of the other vectors is greater than a predefined threshold,the processor may ascertain that the SOI and the subject to whom theother vector belongs have a relatively high likelihood of beingassociated with one another. In response thereto, the processor maygenerate an output that indicates a pairing of the two subjects. Forexample, the output may explicitly state that the two subjects arelikely associated with one another, or it may simply display therespective identities of the two subjects in a manner that implies anassociation. Optionally, the processor may further compute, from therelevant similarity measure, a confidence level that the pair ofsubjects are associated with one another. This confidence level, or“score,” may also be output by the processor.

Alternatively, in response to the computed measures of similarity forthe SOI, the processor may generate an output indicating respectivepairings of the SOI with those of the other subjects whose respectivevectors are most similar to the vector of the SOI. For example, theoutput may include the top N pairings for the SOI, by associating, withthe SOI, a predefined number N of other subjects having the highestmeasures of similarity with respect to the SOI.

As yet another alternative, even without first receiving a queryregarding a particular SOI, the processor may output the top N pairingsfor each of the subjects, and/or any pair of subjects whose measure ofsimilarity is greater than a predefined threshold.

In some embodiments, to save computing time and resources, theprocessor, prior to calculating the measures of similarity, clusters thevectors into a plurality of different clusters, and then selects pairsof vectors in response to each of these pairs being contained within thesame cluster or within respective clusters that are nearby to oneanother (i.e., that are within a predefined threshold distance of oneanother). The processor then computes the measures of similarity for theselected pairs of vectors, but not for other pairs of vectors. Thus, anygiven vector may be compared only to those other vectors that belongeither to the same cluster as the given vector, or to a nearby cluster.For example, if vectors 50 are of K elements each, the processor maypartition the space that is spanned by the vectors into a plurality ofK-dimensional volumes (such as hyperspheres or hypercubes), and thencompare any given vector only with those vectors that are containedwithin the same volume as the given vector, or within a nearby volume.

Reference is now made to FIG. 3, which is a flow diagram for a method 53for identifying associated pairs of subjects, in accordance with someembodiments of the present disclosure. (In general, most of the steps inmethod 53 were already described above, with reference to FIGS. 1-2.)

Method 53 begins with a tracking step 54, at which processor 24 tracksthe locations of a plurality of subjects over several days, weeks,months, or years. Subsequently, the processor, at a weight-computingstep 56, computes the weights for all subjects across all interval-areapairs, i.e., the processor computes a respective weight for eachcombination of a subject, a geographic area, and a time interval. (Atleast some of the weights may be computed during tracking step 54, i.e.,while the tracking is ongoing.) Next, at a subset-selecting step 58, theprocessor selects a subset of the interval-area pairs, which are deemedby the processor to provide more information than the otherinterval-area pairs.

Subsequently, the processor, at a normalizing-factor-computing step 60,computes a normalizing factor for each interval-area pair in theselected subset. Next, at a vector-computing step 62, the processor usesthe normalizing factors to compute a vector of normalized weights foreach of the subjects. In other words, for each selected interval-areapair, the processor normalizes the subset of the weights that correspondto the interval-area pair by the normalizing factor for theinterval-area pair. Subsequently, the processor constructs, for eachsubject, a respective vector that includes those of the normalizedweights that correspond to the subject. As noted above, the processormay then reduce the dimensionality of the vectors.

Subsequently, the processor clusters the vectors, at a clustering step64. Next, the processor identifies associated pairs of subjects. First,the processor selects the vector of an SOI, at a first vector-selectingstep 66. Subsequently, the processor checks, at a vector-identifyingstep 68, whether other vectors belong to the same cluster as theselected vector, or to a nearby cluster. If yes, the processor selects avector of another subject from the same cluster or from a nearbycluster, at a second vector-selecting step 70. Subsequently, theprocessor, at a vector-comparing step 72, checks whether the twoselected vectors are sufficiently close to one another. If yes, theprocessor identifies that the SOI is associated with the other subject,at an association-identifying step 74. The processor then returns tovector-identifying step 68, and, if appropriate, identifies one or moreother subjects who are associated with the SOI.

Following the identification of any subjects who are associated with theSOI, the processor checks, at a subject-of-interest-identifying step 76,whether any other subjects of interest remain. (Each of the subjects ofinterest may be specified by a user, or the processor may simply iteratethrough all of the subjects, treating each of the subjects, in turn, asan SOI.) For each of these other subjects of interest, the processoridentifies any associations, as described above. Following theidentification of any associations for all subjects of interest, theprocessor, at an outputting step 78, outputs all associated pairs ofsubjects.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather, the scope of embodiments of the presentinvention includes both combinations and subcombinations of the variousfeatures described hereinabove, as well as variations and modificationsthereof that are not in the prior art, which would occur to personsskilled in the art upon reading the foregoing description. Documentsincorporated by reference in the present patent application are to beconsidered an integral part of the application except that to the extentany terms are defined in these incorporated documents in a manner thatconflicts with the definitions made explicitly or implicitly in thepresent specification, only the definitions in the present specificationshould be considered.

1.-20 (canceled)
 21. A system, comprising: a communication interface;and a processor, executing instructions to: receive, via thecommunication interface, tracking data that indicates respectivelocations of a plurality of subjects within a plurality of geographicareas and during a plurality of time intervals; calculate, via theprocessor, a plurality of weights that respectively correspond to eachcombination of a subject, a geographic area, and a time interval;select, via the processor, a plurality of interval-area pairs, each ofwhich includes a respective one of the geographic areas and a respectiveone of the time intervals; normalize, for each of the selectedinterval-area pairs, via the processor, a plurality of vectors thatcorrespond to respective combinations of the subject with the selectedinterval-area pairs cluster, via the processor, the plurality of vectorsinto a plurality of different clusters, determine, via the processor,one of more pairs of vectors, wherein any given vector is paired only tovectors that belong either to the same cluster as the given vector, orare within a predefined threshold distance of one cluster to anadditional cluster; determine, via the processor, measures of similaritybetween one or more pairs of the vectors; and identify, in response tothe measures of similarity, an association of the subject and at leastone other subject of interest.
 22. The system of claim 21, wherein theprocessor further executes instructions to receive the tracking datafrom a cellular the network.
 23. The system of claim 21, wherein theprocessor further executes instructions to maintain a location historyfor the plurality of subjects.
 24. The system of claim 21, wherein theprocessor further executes instructions to calculate each of the weightsresponsively to a percentage of the time interval during which thesubject was located in the geographic area that is indicated by thetracking data.
 25. The system of claim 21, wherein the processor furtherexecutes instructions to calculate each of the weights by: calculating alevel of confidence that the tracking data indicate that the subject waslocated in the geographic area during the time interval; and calculatingthe weight responsively to the level of confidence.
 26. The system ofclaim 21, wherein the processor further executes instructions tonormalize the respective subset of the weights by: calculating anormalizing factor as (i) an increasing function of a sum of the subsetof the weights, and (ii) a decreasing function of a total sum of theweights; and normalizing the subset of the weights by the normalizingfactor.
 27. The system of claim 21, wherein the processor furtherexecutes instructions to normalize the respective subset of the weightsby: calculating a normalizing factor as (i) an increasing function of anumber of those of the weights in the subset that are greater than zero,and (ii) a decreasing function of a total number of the subjects; andnormalizing each of the weights in the subset by the normalizing factor.28. The system of claim 21, wherein the processor further executesinstructions to, prior to calculating the measures of similarity:clusters the vectors into a plurality of different clusters; andsubsequently to clustering the vectors, select the pairs of the vectorsin response to each of the pairs of the vectors being contained within asame one of the clusters or within respective ones of the clusters thatare within a predefined threshold distance of one another.
 29. Thesystem of claim 21, wherein the processor further executes instructionsto output the association of the subject and the at least one othersubject, and to visually display the output.
 30. A non-transitorycomputer readable having computer executable instructions stored thereonthat when execute by a processor causes the processor to: receive, via acommunication interface, tracking data that indicates respectivelocations of a plurality of subjects within a plurality of geographicareas and during a plurality of time intervals; calculate, via theprocessor, a plurality of weights that respectively correspond to eachcombination of a subject, a geographic area, and a time interval;select, via the processor, a plurality of interval-area pairs, each ofwhich includes a respective one of the geographic areas and a respectiveone of the time intervals; normalize, for each of the selectedinterval-area pairs, via the processor, a plurality of vectors thatcorrespond to respective combinations of the subject with the selectedinterval-area pairs cluster, via the processor, the plurality of vectorsinto a plurality of different clusters, determine, via the processor,one of more pairs of vectors, wherein any given vector is paired only tovectors that belong either to the same cluster as the given vector, orare within a predefined threshold distance of one cluster to anadditional cluster; determine, via the processor, measures of similaritybetween one or more pairs of the vectors; and identify, in response tothe measures of similarity, an association of the subject and at leastone other subject of interest.
 31. A method, comprising: providing acommunication interface; providing a processor; receiving, via acommunication interface, tracking data that indicates respectivelocations of a plurality of subjects within a plurality of geographicareas and during a plurality of time intervals; calculating, via theprocessor, a plurality of weights that respectively correspond to eachcombination of a subject, a geographic area, and a time interval;selecting, via the processor, a plurality of interval-area pairs, eachof which includes a respective one of the geographic areas and arespective one of the time intervals; normalizing, for each of theselected interval-area pairs, via the processor, a plurality of vectorsthat correspond to respective combinations of the subject with theselected interval-area pairs clustering, via the processor, theplurality of vectors into a plurality of different clusters,determining, via the processor, one of more pairs of vectors, whereinany given vector is paired only to vectors that belong either to thesame cluster as the given vector, or are within a predefined thresholddistance of one cluster to an additional cluster; determining, via theprocessor, measures of similarity between one or more pairs of thevectors; and identifying, in response to the measures of similarity, anassociation of the subject and at least one other subject of interest.32. The method of claim 31, wherein the method further comprisesreceiving the tracking data from a cellular the network.
 33. The methodof claim 31, wherein the method further comprises maintaining a locationhistory for the plurality of subjects.
 34. The method of claim 31,wherein the method further comprises calculating each of the weightsresponsively to a percentage of the time interval during which thesubject was located in the geographic area that is indicated by thetracking data.
 35. The method of claim 31, wherein the method furthercomprises calculating each of the weights by: calculating a level ofconfidence that the tracking data indicate that the subject was locatedin the geographic area during the time interval; and calculating theweight responsively to the level of confidence.
 36. The method of claim31, wherein the method further comprises normalizing the respectivesubset of the weights by: calculating a normalizing factor as (i) anincreasing function of a sum of the subset of the weights, and (ii) adecreasing function of a total sum of the weights; and normalizing thesubset of the weights by the normalizing factor.
 37. The method of claim31, wherein the method further comprises normalizing the respectivesubset of the weights by: calculating a normalizing factor as (i) anincreasing function of a number of those of the weights in the subsetthat are greater than zero, and (ii) a decreasing function of a totalnumber of the subjects; and normalizing each of the weights in thesubset by the normalizing factor.
 38. The method of claim 31, whereinthe method further comprises outputting the association of the subjectand the at least one other subject, and to visually display the output.